medical image computing (cap 5937)bagci/teaching/mic16/lec17.pdfmedical image computing (cap 5937)...

MEDICAL IMAGE COMPUTING (CAP 5937)

LECTURE 17: Medical Image Registration III (Advanced):FFD with B-Splines, Diffeomorphic Image Registration, and

Regularizations

Dr. Ulas BagciHEC 221, Center for Research in Computer Vision (CRCV), University of Central Florida (UCF), Orlando, FL [email protected] or [email protected]

1SPRING 2016

Outline• Deformable Image Registration

– B-spline parametrization and Free Form Deformation• Optimization • Diffeomorphic Image Registration

2

Rigid Transformation

3

• Rotation• Translation• Scale 12 pRstp !"!! +=

⎥⎦

⎤⎢⎣

⎡ −=)cos()sin(

)sin()cos(

θθθθ

R

⎥⎦

⎤⎢⎣

⎡=

2

22 y

xp!⎥

⎦

⎤⎢⎣

⎡=

1

11 y

xp! ⎥

⎦

⎤⎢⎣

⎡=

2

11 s

ss! ⎥

⎦

⎤⎢⎣

⎡=

2

11 t

tt!

Rigid Transformation

4

Affine Transformation

5

• Rotation• Translation• Scale• Shear

– No more preservation of lengths and angles– Parallel lines are preserved

⎥⎦

⎤⎢⎣

⎡⎥⎦

⎤⎢⎣

⎡++

+⎥⎦

⎤⎢⎣

⎡=⎥

⎦

⎤⎢⎣

⎡

1

1

2221

1211

23

13

2

2

yx

aaaa

aa

yx

Non-Rigid Deformation

6

Source

Target

Before

After

Deformation Fields

7

y

x

Deformation Fields

8

y

x

Free Form Deformation

9

Credits: Sederberg and Parry, SIGGRAPH (1986)

Global Motion Model

10

(12 degrees of freedom)

Local Motion Model• The affine transformation captures only the global motion.• An additional transformation is required, which models the

local deformation

11

Local Motion Model• The affine transformation captures only the global motion.• An additional transformation is required, which models the

local deformation

12

FFD with B-Splines

13

B-Spline / Math

14

Deformation with B-Splines

16

Original Lena

Deformation with B-Splines

17

Deformed with B-Spline - Lena

B-Spline Parametrization

18

B-Splines Practically

19

B-Splines Practically

20

Optimization

21

Math-Defn.

22

Iterative Optimization

23

Gradient Descent

24

Cost Function Derivative

25

Diffeomorphic Image Registration• At its simplest, image registration involves estimating a

smooth, continuous mapping between the points in one image and those in another.

30



• The relative shapes of the images can then be determined from the parameters that encode the mapping.

31




• The objective is usually to determine the single “best” set of values for these parameters

32




• The objective is usually to determine the single “best” set of values for these parameters– The small-deformation framework does not necessarily preserve

topology—although if the deformations are relatively small, then it may still be preserved.

– The large-deformation framework generates deformations (diffeomorphisms) that have a number of elegant mathematical properties, such as enforcing the preservation of topology.

33

Deformation Model• Many registration algorithm still use small displacement

models (u), which is simply added into identity transform (x):

34

Deformation Model• Many registration algorithm still use small displacement

models (u), which is simply added into identity transform (x):

• In such parameterizations, the inverse transformation is sometimes approximated by subtracting the displacement. It is worth noting that this is only a very approximate inverse, which fails badly for larger deformations.

35

Deformation Model

36

Small deformation models do not necessarily enforce a one-to-one mapping.

If the inverse transformation is correct,these two should be identity!

Diffeomorphic Registration• The large-deformation or diffeomorphic setting is a much

more elegant framework. A diffeomorphism is a globally one-to-one (objective) smooth and continuous mapping with derivatives that are invertible.

37



• If the mapping is not diffeomorphic, then topology is not necessarily preserved.

38




• it is easier to parameterize using a number of velocity fields corresponding to different time periods over the course of the evolution of the diffeomorphism (consider u(t) as a velocity field at time t

39




• it is easier to parameterize using a number of velocity fields corresponding to different time periods over the course of the evolution of the diffeomorphism (consider u(t) as a velocity field at time t

• Diffeomorphisms are generated by initializing with an identity transform and integrating over unit time to obtain

40

Forward & Inverse Transform

41

DARTEL: A Fast Diffeomorphic Method (Ashburner, NeuroImage 2007)

DiffeomorphicAnatomicalRegistrationThroughExponentiatedLie Algebra

42


• The DARTEL model assumes a flow field (u) that remains constant over time. With this model, the differential equation describing the evolution of a deformation is

43



• The Euler method is a simple integration approach to extend from identity (initial) transform, which involves computing new solutions after many successive small time-steps (h).

44



• The Euler method is a simple integration approach to extend from identity (initial) transform, which involves computing new solutions after many successive small time-steps (h).

• Each of these Euler steps is equivalent to

45


• The use of a large number of small time steps will produce a more accurate solution, for instance (8 steps)

46

Optimization of DARTEL• Image registration procedures use a mathematical model to

explain the data. Such a model will contain a number of unknown parameters that describe how an image is deformed.

47



• A true diffeomorphism has an infinite number of dimensions and is infinitely differential.

48



• A true diffeomorphism has an infinite number of dimensions and is infinitely differential.

• The discrete parameterization of the velocity field, u(x), can be considered as a linear combination of basis functions.

49

ρi(x) is the ith first degree B-spline basis function at position x

v is a vector of coefficients

Optimization of DARTEL• The aim is to estimate the single “best” set of values for these

parameters (v). The objective function, which is the measure of “goodness”, is formulated as the most probable deformation, given the data (D).

50



• The objective is to find the most probable parameter values and not the actual probability density, so this factor is ignored. The single most probable estimate of the parameters is known as the maximum a posteriori (MAP) estimate.

51



• The objective is to find the most probable parameter values and not the actual probability density, so this factor is ignored. The single most probable estimate of the parameters is known as the maximum a posteriori (MAP) estimate.

Or

52

Optimization of DARTEL• Many nonlinear registration approaches search for a

maximum a posteriori (MAP) estimate of the parameters defining the warps, which corresponds to the mode of the probability density

53



• The Levenberg–Marquardt (LM) algorithm is a very good general purpose optimization strategy

54



• The Levenberg–Marquardt (LM) algorithm is a very good general purpose optimization strategy

55

A: second order tensor field, H: concentration matrix, b: first derivative of likelihood func.

Optimization of DARTELSimultaneously minimize the sum of

– Likelihood component• Sum of squares difference

• ½ ∑i∑k(tk(xi) – μk(φ(1)(xi)))2

• φ(1) parameterized by u– Prior component

• A measure of deformation roughness

• ½uTHu

56

Optimization of DARTELPRIOR TERM• ½uTHu• DARTEL has three different models for H

– Membrane energy– Linear elasticity– Bending energy

• H is very sparse• H: deformation roughness

57

An example H for 2D registration of 6x6 images (linear elasticity)

Optimization of DARTEL

LIKELIHOOD TERM• Images assumed to be partitioned into different tissue

classes.– E.g., a 3 class registration simultaneously matches:

• Grey matter with grey matter• White matter wit white matter• Background (1 – GM – WM) with background

58

“Membrane Energy”

Convolution KernelSparse Matrix Representation

Penalizes first derivatives.Sum of squares of the elements of the Jacobian (matrices) of the flow field.

“Bending Energy”

Sparse Matrix Representation Convolution Kernel

Penalizes second derivatives.

“Linear Elasticity”• Decompose the Jacobian of the flow field into

– Symmetric component• ½(J+JT)• Encodes non-rigid part.

– Anti-symmetric component• ½(J-JT)• Encodes rigid-body part.

• Penalise sum of squaresof symmetric part.

• Trace of Jacobianencodes volume changes.Also penalized.

Gauss-Newton Optimization• Uses Gauss-Newton

– Requires a matrix solution to a very large set of equations at each iteration

u(k+1) = u(k) - (H+A)-1 b

– b are the first derivatives of objective function– A is a sparse matrix of second derivatives– Computed efficiently, making use of scaling and squaring

62

Summary• Deformable Image Registration

– B-spline parametrization and Free Form Deformation• Optimization • Diffeomorphic Image Registration

63

Slide Credits and References• Darko Zikic, MICCAI 2010 Tutorial• Stefan Klein, MICCAI 2010 Tutorial• Marius Staring, MICCAI 2010 Tutorial• J. Ashburner, NeuroImage 2007.• M. F. Beg, M. I. Miller, A. Trouvé and L. Younes. “Computing

Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms”. International Journal of Computer Vision 61(2):139–157 (2005).

• M. Vaillant, M. I. Miller, L. Younes and A. Trouvé. “Statistics on diffeomorphisms via tangent space representations”. NeuroImage 23:S161–S169 (2004).

• L. Younes, “Jacobi fields in groups of diffeomorphisms and applications”. Quart. Appl. Math. 65:113–134 (2007).

64

medical image computing (cap 5937)bagci/teaching/mic16/lec17.pdfmedical image computing (cap 5937)...

Documents